APPLICATIONS OF SPECTRAL ANALYSIS

The basic concepts and application of spectral analysis are explained. Stationary time series and autocorrelation are first defined. Autocorrelation is related to the familiar concepts of variance and covariance. The use of autocorrelation analysis is explained in estimating the interdependent relationship of a time series over discrete time lags. In order to measure the behavior of the time series using autocorrelation, it would be necessary to examine a very large number of autocorrelation lags. Alternatively, the technique of Fourier analysis can be used to transform the autocorrelation function of the time series into a continuous function, termed a spectrum. The spectrum has a one to one correspondence to the autocorrelation for the time series and has the advantage of representing all possible autocorrelations over the discrete time lags. The spectrum can then be examined as a measure of the behavior of the time series. Spectral analysis indicates the reliability of the analysis of autocorrelated variables when familiar statistical techniques such as sample means and variances are used. The application of spectral analysis to management science problems in three general areas is illustrated: (1) inventory demand, (2) transportation simulation, and (3) stock market price behavior. Spectral analysis was used to detect cycles and trends in the data. Analyses were focused on the spectrum which provides a measure of the relative contribution of cycles in a band of frequencies to the total variance of the data.     

Models for Dependent Extremes Using Stable Mixtures

Abstract.  This paper unifies and extends results on a class of multivariate extreme value (EV) models studied by Hougaard, Crowder and Tawn. In these models, both unconditional and conditional distributions are themselves EV distributions, and all lower-dimensional marginals and maxima belong to the class. One interpretation of the models is as size mixtures of EV distributions, where the mixing is by positive stable distributions. A second interpretation is as exponential-stable location mixtures (for Gumbel) or as power-stable scale mixtures (for non-Gumbel EV distributions). A third interpretation is through a peaks over thresholds model with a positive stable intensity. The mixing variables are used as a modelling tool and for better understanding and model checking. We study EV analogues of components of variance models, and new time series, spatial and continuous parameter models for extreme values. The results are applied to data from a pitting corrosion investigation.     

全球商业革命、瀑布效应以及中国企业面临的挑战

20世纪末发生的全球商业革命具有三大特征：一是在跨国公司重组并购浪潮中所形成的高度产业集中．二是由核心公司对其所在价值链所进行的“系统集成”的管理手段,三是在系统集成过程中所产生的对价值链各个层面的巨大的集约压力,即所谓的“瀑布效应”。文章通过对航空工业和饮料业两个截然不同的产业的案例分析来显示这几个特征的普遍性。这些变化对正在步入世界商业体系的中国企业带来了巨大的挑战。     

The abuse of children in out of home care

SUMMARY: One of the major paradoxes in child protective services in the United States is that children removed from their homes on protective orders and placed in foster and group care face continued abuse. These children are reported at two to three times the rates of children living with their families. Investigations are performed by the very system that marks placements. In the US, some recommend resolving these problems by withdrawing from the active protection of children in care. Others hold that because children are in the care and custody of the state, and demand a higher standard of care, specialised prevention, identification. reporting and investigation initiatives are necessary to ensure their safety. This has important implications for other countries, especially the United Kingdom with its recent spate of inquiries into abuse in residential care.